Data-driven manufacturing: An assessment model for data science maturity


Gökalp M. O., GÖKALP AYDIN E., KAYABAY K., KOÇYİĞİT A., EREN P. E.

Journal of Manufacturing Systems, vol.60, pp.527-546, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jmsy.2021.07.011
  • Journal Name: Journal of Manufacturing Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.527-546
  • Keywords: Smart manufacturing, Industry 4, 0, Maturity model, Data science, Maturity assessment, Process improvement
  • Hacettepe University Affiliated: Yes

Abstract

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.